The performance of two mothers wavelets in function approximation.

Research into Wavelet Neural Networks was conducted on numerous occasions in the past. Based on previous research, it was noted that the Wavelet Neural Network could reliably be used for function approximation. The research conducted included comparisons between the mother functions of the Wavelet Neural Network namely the Mexican Hat, Gaussian Wavelet and Morlet Functions. The performances of these functions were estimated using the Normalised Square Root Mean Squared Error (NSRMSE) performance index. However, in this paper, the Root Mean Squared Error (RMSE) was used as the performance index. In previous research, two of the best mother wavelets for function approximations were determined to be the Gaussian Wavelet and Morlet functions. An in-depth investigation into the two functions was conducted in order to determine which of these two functions performed better under certain conditions. Simulations involving one-dimension and two-dimension were done using both functions. In this paper, we can make a specifically interpretation that Gaussian Wavelet can be used for approximating function for the function domain [−1, 1]. While Morlet function can be used for big domain. All simulations were done using Matlab V6.5

Development of Individual Learners: Perspective on the Uncertain Future Contribution of E-Learning

E-learning in the educational world has grown and changed rapidly in recent years. Both private and public sector organizations have embraced the practice of reaching their students at a distance via new technologies afforded them by Information Communication Technology (ICT) infrastructure. E-learning is grounded on technology, and without it, its practice would be difficult, if not impossible. We can see that the use of the internet and ICT are becoming an important part of learning and teaching strategies in many educational institutions. Knowing that education has always been an important engine for economic development, the Malaysian government has taken initiative steps to implement public awareness on the ICT issues. An important part of e-learning has been to contribute to the development of individual learners whatever their life circumstances. E-learning as a field of educational endeavor is at a crucial juncture in its historical development. The notion of learning at a distance has gained wide acceptance across the developed world. Instructors, physically and temporally separated from learners using newly emerging information and communication technologies, are widespread. The potential of latest technology has adopted in creating new learning environments. The rational behind this endeavor is the expectation that unique features of the Information Communication technology. It also can include a range of powerful media forms and its interactive capability that support sophisticated range and interaction in teaching. As a result, these approaches will provide a rich environment for teaching

Preparation and properties of natural rubber/layered double hydroxide nanocomposites

Nanocomposites of natural rubber (NR)/organo ZnAl layered double hydroxide (ZnAl LDH-DS) were successfully synthesized and characterized. A hydrophilic Zn-Al layered double hydroxide (ZnAl LDH-NO3-) was converted into the organophilic form by replacing the nitrate ion in between the ZnAl LDH-NO3- with dodecylsulfate ion (DS) to form ZnAl LDH-DS. The melt intercalation technique followed by vulcanization process was adopted to synthesize the nanocomposites of NR/ZnAl LDH-DS. Intercalation of DS ion into the interlayer of ZnAl LDH-NO3- increased the surface area and the porosity of the LDH. X-Ray diffractogram of the organophilic ZnAl LDH-DS shows that the basal spacing of the ZnAl LDH-NO3- expands from 0.89 to 2.53 nm due to the accommodation of DS ion in the intergallery. After the compounding process with the NR, the basal spacing of ZnAl LDH-DS in the composites is increased to 3.90 and 3.27 nm when the ZnAl LDH-DS contents were 1 and 15 phr, respectively. Transmission electron microscope image revealed that the ZnAl LDH-DS was distributed in the NR matrix in such a way of exfoliated and different grade of intercalated. The tensile strength values of NR/ ZnAl LDH-DS (nanocomposites) were found to be higher than that of the NR/ZnAl LDH-NO3- (macrocomposites)

Preparation and characterization of natural rubber/layered double hydroxide nanocomposites

Nanocomposites of organo Zn‐Al layered double hydroxide (LDH) with natural rubber (SMR CV60) were successfully synthesized and characterized. To prepare the nanocomposites, a hydrophilic Zn‐Al layer double hydroxide (ZnAl LDH ‐NO3−) was first converted into the organophilic form by using dodecylsulphate ion (DS) as a guest in Zn‐Al layer double hydroxide (ZnAl LDH‐DS). Intercalation of dodecylsulphate anion into the interlayer of LDH increased the surface area and the porosity of LDH. Nanocomposites of NR / ZnAl LDH‐DS was then prepared by melt intercalation method using Haake internal mixer. The resulting compounds were then vulcanizated using the conventional method. X‐Ray diffractogram the organophilic ZnAl‐DS LDH shows the basal spacing of the ZnAl‐LDH expands from 0.89 nm with nitrate as the intergallery anions to 2.53 nm due to the accommodation of DS surfactant anions. After the compounding with the natural rubber, the basal spacing of ZnAl LDH‐DS in the composites is increased to 3.90 and 3.66 nm when the Zn‐Al‐LDH‐DS contents are 1 phr and 15 phr respectively. TEM revealed the layered double hydroxide generally uniformly distributed in the rubber matrix. Further characterization indicates that the tensile strength of NR/ Zn‐Al LDH‐DS (nanocomposites) is higher than that of the NR/Zn‐Al LDH‐NO3− (macrocomposites)

Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)

The aim this study is discussed on the detection and correction of data containing the additive outlier (AO) on the model ARIMA (p, d, q). The process of detection and correction of data using an iterative procedure popularized by Box, Jenkins, and Reinsel (1994). By using this method we obtained an ARIMA models were fit to the data containing AO, this model is added to the original model of ARIMA coefficients obtained from the iteration process using regression methods. This shows that there is an improvement of forecasting error rate data.Comment: 13 page